Initializing

import numpy as np
import pandas as pd
import warnings
from scipy.stats import pearsonr
from tqdm import tqdm


warnings.filterwarnings('ignore')

# reminder that if you are installing libraries in a Google Colab instance you will be prompted to restart your kernal

try:
    import geemap, ee
    import seaborn as sns
    import matplotlib.izingpyplot as plt
except ModuleNotFoundError:
    if 'google.colab' in str(get_ipython()):
        print("package not found, installing w/ pip in Google Colab...")
        !pip install geemap seaborn matplotlib
    else:
        print("package not found, installing w/ conda...")
        !conda install mamba -c conda-forge -y
        !mamba install geemap -c conda-forge -y
        !conda install seaborn matplotlib -y
    import geemap, ee
    import seaborn as sns
    import matplotlib.pyplot as plt

package not found, installing w/ pip in Google Colab...
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try:
        ee.Initialize()
except Exception as e:
        ee.Authenticate()
        ee.Initialize()

*** Earth Engine *** Share your feedback by taking our Annual Developer Satisfaction Survey: https://google.qualtrics.com/jfe/form/SV_doiqkQG3NJ1t8IS?source=API

!pip install cmasher
import geocoder
import cmasher as cmr
import geemap.colormaps as cm

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from google.colab import auth
import gspread
from google.auth import default
#autenticating to google
auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)

import matplotlib.dates as mdates
from matplotlib import cm

from tqdm import tqdm
import itertools
import statsmodels.api as sm
import warnings
warnings.filterwarnings('ignore')
from matplotlib import gridspec
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter

import datetime
import matplotlib.ticker as mtick
from scipy import stats

import numpy as np
import pandas as pd
import warnings
from scipy.stats import pearsonr

# import geocoder
# import geemap.colormaps as cm

# !pip install geopandas
# import geopandas as gpd

from google.colab import drive
drive.mount('/content/gdrive')

Mounted at /content/gdrive

import os

Displaying shapefiles

# # gps_path = '/content/gdrive/My Drive/Research - MS/Codes/MS Thesis/MS-Thesis/Shape_Files/PHGE71FL/PHGE71FL.shp'
# # geopath = gpd.read_file(geopath)
# path = '/content/gdrive/My Drive/Research - MS/Codes/MS Thesis/MS-Thesis/Shape_Files/PhilGIS/'
# sf_gpd_country = gpd.read_file(path + "Country (extract.me)/Country.shp")
# sf_gpd_regions = gpd.read_file(path + "Regions (extract.me)/Regions.shp")

# sf_gpd_municities = gpd.read_file(path + "MuniCities/MuniCities.shp")
# sf_gpd_provinces = gpd.read_file(path + "Provinces/Provinces.shp")
# sf_gpd_roads = gpd.read_file(path + "Roads/roads.shp")

# plt.figure(dpi = 200)
# plt.subplot(151)
# sf_gpd_country.plot(edgecolor="black", linewidth = 0.5, facecolor="None", ax = plt.gca())
# plt.axis("off")
# plt.subplot(152)
# sf_gpd_regions.plot(edgecolor="red", linewidth = 0.5, facecolor="None", ax = plt.gca())
# plt.rc('font', size = 5)
# plt.axis("off")
# plt.subplot(153)
# sf_gpd_provinces.plot(edgecolor="orange", linewidth = 0.5, facecolor="None", ax = plt.gca())
# plt.rc('font', size = 5)
# plt.axis("off")
# plt.subplot(154)
# sf_gpd_municities.plot(edgecolor="green", linewidth = 0.5, facecolor="None", ax = plt.gca())
# plt.rc('font', size = 5)
# plt.axis("off")
# plt.subplot(155)
# sf_gpd_roads.plot(edgecolor="blue", linewidth = 0.5, facecolor="None", ax = plt.gca())
# plt.rc('font', size = 5)
# plt.axis("off")
# plt.tight_layout()

time_series_()

def time_series_(lat, lon, buffer):
    def poi_mean(img):
        img = img.updateMask(img.gte(0))
        mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=poi, scale=500).get('avg_rad')
        return img.set('date', img.date().format()).set('mean',mean)

    poi = ee.Geometry.Point(lat, lon).buffer(buffer)
    viirs = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG").filterDate('2014-01-01','2023-12-31')
    poi_reduced_imgs = viirs.map(poi_mean)
    nested_list = poi_reduced_imgs.reduceColumns(ee.Reducer.toList(1), ['mean']).values().get(0)
    data = pd.DataFrame(nested_list.getInfo(), columns=['mean'])
    data = data['mean'].values.tolist()
    return data

def time_series_(lat, lon, buffer):
    def poi_mean(img):
        img = img.updateMask(img.gte(0))
        mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=poi, scale=500).get('avg_rad')
        return img.set('date', img.date().format()).set('mean',mean)

    poi = ee.Geometry.Point(lat, lon).buffer(buffer)
    viirs = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG").filterDate('2014-01-01','2023-12-31')
    poi_reduced_imgs = viirs.map(poi_mean)
    nested_list = poi_reduced_imgs.reduceColumns(ee.Reducer.toList(2), ['date', 'mean']).values().get(0)
    data = pd.DataFrame(nested_list.getInfo(), columns=['date','mean'])
    data = data.set_index('date')

    # data = data['mean'].values.tolist()
    return data

  nested_list = prov_mean.reduceColumns(ee.Reducer.toList(2), ['date','mean']).values().get(0)
  nested_list_2 = reg_mean.reduceColumns(ee.Reducer.toList(1), ['mean']).values().get(0)
  regional_NTL = np.asarray(nested_list_2.getInfo()).squeeze()

  # convert to dataframe
  NTL_dataset = pd.DataFrame(nested_list.getInfo(), columns=['date', str(Province) + ' Province'])
  NTL_dataset['date'] = pd.to_datetime(NTL_dataset['date'])
  NTL_dataset = NTL_dataset.set_index('date')
  NTL_dataset.insert(1, str(reg_names[reg_codes.index(Region)]), regional_NTL)

Loading Databse

#defining my worksheet
worksheet = gc.open('List of Housing Projects').sheet1
rows = worksheet.get_all_values()

#Convert to a DataFrame
df = pd.DataFrame(rows)
#creating columns name
df.columns = df.iloc[0]
df = df.iloc[1:]
#convert 'strings' to 'float'
df['Latitude'] = pd.to_numeric(df['Latitude'], errors='coerce')
df['Longitude'] = pd.to_numeric(df['Longitude'], errors='coerce')
df['Buffer'] = pd.to_numeric(df['Buffer'], errors='coerce')

# df['Latitude'] = df['Latitude'].fillna(0).astype(float)
# df['Longitude'] = df['Longitude'].fillna(0).astype(float)
# df['Buffer'] = df['Buffer'].fillna(0).astype(float)

df = df[df['Buffer'].notna()].reset_index()

# df.dropna(subset=['Buffer'], inplace=True)

df

	index	Typhoon	Province	Project Name	Location	Latitude	Longitude	Buffer	Classification	Full Project Title
0	1	Yolanda	Cebu	Maya Park	Brgy. Maya, Daanbantayan, Cebu	11.271009	124.048967	230.0	Identified	Maya Park Homes
1	2	Yolanda	Cebu	Lanao Unity Park	Brgy. Lanao, Daanbantayan, Cebu	11.248469	124.022224	280.0	Identified	Lanao Unity Park Homes
2	3	Yolanda	Cebu	Daanbantayan 3	Brgy. Tominjao, Daanbantayan, Cebu	11.221464	124.036788	260.0	Probable	Green Heights 1 (Daanbantayan 3)
3	4	Yolanda	Cebu	Daanbantayan 4	Brgy. Tominjao, Daanbantayan, Cebu	11.173075	124.015380	160.0	Identified	Green Heights 2 (Daanbantayan 4)
4	5	Yolanda	Cebu	Daanbantayan Site 1	Brgy. Tapilon, Daanbantayan Site 1, Cebu	11.260164	124.036611	160.0	Identified	Kandaya Homes 1 (Daanbantayan Site 1)
...	...	...	...	...	...	...	...	...	...	...	...	...	...
225	286	Reming	Albay	Polangui Resettlement Project	Lanigay, Polangui, Albay	13.317859	123.480334	220.0	Probable	Polangui Resettlement Project (Cielo Ville Ka...
226	287	Reming	Albay	Quitago Resettlement Project	Quitago, Guinobatan, Albay	13.169667	123.607585	240.0	Identified	Quitago Resettlement Project
227	288	Reming	Albay	Mauraro Resettlement Project	Mauraro, Guinobatan, Albay	13.141681	123.598459	240.0	Identified	Mauraro Resettlement Project
228	289	Reming	Albay	Bascaran Resettlement Project	Bascaran, Daraga, Albay	13.097414	123.695317	110.0	Probable	Bascaran Resettlement Project
229	290	Reming	Albay	Baligang Resettlement Project	Baligang, Camalig, Albay	13.133385	123.637592	300.0	Identified	Baligang Resettlement Project

230 rows × 13 columns

Database Statistics

import matplotlib.pyplot as plt
import seaborn as sns

# plt.figure(figsize = (15,3), dpi = 150)

g = sns.catplot(y = 'Typhoon', data = df, kind='count',
                palette = 'gnuplot2', order = df['Typhoon'].value_counts().index)
plt.grid(alpha = 0.5)

ax = g.facet_axis(0, 0)

for c in ax.containers:
    labels = [f'{(v.get_width()):1.0f}' for v in c]
    ax.bar_label(c, labels=labels, label_type='edge')
plt.xlabel('Number of resettlement sites')
plt.show()

g = sns.catplot(y = 'Province', data = df, kind='count',
                palette = 'viridis', order = df['Province'].value_counts().index)
plt.grid(alpha = 0.5)

ax = g.facet_axis(0, 0)

for c in ax.containers:
    labels = [f'{(v.get_width()):1.0f}' for v in c]
    ax.bar_label(c, labels=labels, label_type='edge')
plt.xlabel('Number of resettlement sites')
plt.show()

plot_size = 5

pal = list(sns.color_palette(palette='gnuplot2_r', n_colors=3).as_hex())
plt.figure(dpi = 150, facecolor = None)



plt.gcf().set_size_inches(plot_size, plot_size*1.5)
ax = plt.subplot(projection='polar')
# input_df.reset_index(inplace=True, drop=True)
numbers = [210, 334, 468]
list_details = ['Indentified + Probable sites', 'Unique sites', 'Original NHA List']

ax.set_theta_zero_location('N')
ax.set_theta_direction(1)
ax.set_rlabel_position(0)
ax.set_thetagrids([], labels=[])
ax.set_thetagrids(np.arange(0,360,36), labels=(np.arange(0,360,36)*50/36).astype(int), fontsize = 8)

for i in range(3):
    ax.barh(i, numbers[i]*2*np.pi/500, label=list_details[i], color=pal[i])

plt.legend(loc = 0, fontsize=15, bbox_to_anchor=(1, -0.1))

ax.set_rgrids(range(3), labels = numbers, fontsize = 15, color = 'r')
ax = plt.subplot(projection='polar')


# plt.title(column_name + ": " + str(category))
# plt.tight_layout()
# plt.savefig(results + str(category) + ".png")
# plt.show()

MapViewer

reg_names =  ['NCR',
              'CAR',
              'Region I',
              'Region II',
              'Region III',
              'Region IV-A',
              'Region IV-B',
              'Region V',
              'Region VI',
              'Region VII',
              'Region VIII',
              'Region IX',
              'Region X',
              'Region XI',
              'Region XII',
              'Region XIII',
              'ARMM']
reg_codes =  [2355, 2354, 2356, 2357,
              67165, 67166, 67167,
              2361, 2362, 2363, 2364,
              67159, 67160, 67161, 67162,
              2368, 67156]

Regional NTL

# viirs_ph = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG").select('avg_rad')
# reg_geoms = ee.FeatureCollection("FAO/GAUL/2015/level1").filter(ee.Filter.inList('ADM1_CODE', reg_codes))

# # function to reduce our collection of geometries'
# def get_reg_avg_rad(img):
#     return img.reduceRegions(reducer=ee.Reducer.mean(), collection=reg_geoms, scale=500)

# # function to get individual image dates
# def get_date(img):
#     return img.set('date', img.date().format())

# # map these functions to our image collection
# reduced_regions = viirs_ph.map(get_reg_avg_rad).flatten()
# dates = viirs_ph.map(get_date)

# # get lists
# key_cols = ['ADM1_CODE','mean']

# regions_list = reduced_regions.reduceColumns(ee.Reducer.toList(len(key_cols)), key_cols).values()
# dates_list = dates.reduceColumns(ee.Reducer.toList(1), ['date']).values()

# # some numpy maneuvers to structure our data
# df_ph = pd.DataFrame(np.asarray(regions_list.getInfo()).squeeze(), columns=key_cols)
# dates = np.asarray(dates_list.getInfo()).squeeze()

# for regions in tqdm(reg_codes, 'Processing Regional Extraction:'):
#     df_ph.loc[df_ph['ADM1_CODE']==regions,'dates'] = dates

# df_ph['dates'] = pd.to_datetime(df_ph['dates'])
# df_ph.set_index('dates', inplace=True)

# df_ph['mean'] = df_ph['mean'].astype(float)

# df_ph

Resettlement NTL Extraction

NTL_dataset_initialize(Province, Region)

def NTL_dataset_initialize(Province, Region):

  viirs = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG').filter(ee.Filter.date('2014-01-01', '2023-12-01'))
  province = ee.Feature(ee.FeatureCollection("FAO/GAUL/2015/level2").filter(ee.Filter.eq('ADM2_NAME', Province)).first()).geometry()
  region = ee.Feature(ee.FeatureCollection("FAO/GAUL/2015/level1").filter(ee.Filter.eq('ADM1_CODE', Region)).first()).geometry()

  def get_prov_mean(img):
      img = img.updateMask(img.gte(0))
      mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=province, scale=500, maxPixels=1e9).get('avg_rad')
      return img.set('date', img.date().format()).set('mean',mean)

  def get_reg_mean(img):
      img = img.updateMask(img.gte(0))
      mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=500, maxPixels=1e9).get('avg_rad')
      return img.set('date', img.date().format()).set('mean',mean)

  # reduce collection
  prov_mean = viirs.map(get_prov_mean)
  reg_mean = viirs.map(get_reg_mean)

  # get lists
  nested_list = prov_mean.reduceColumns(ee.Reducer.toList(2), ['date','mean']).values().get(0)
  nested_list_2 = reg_mean.reduceColumns(ee.Reducer.toList(1), ['mean']).values().get(0)
  regional_NTL = np.asarray(nested_list_2.getInfo()).squeeze()

  # convert to dataframe
  NTL_dataset = pd.DataFrame(nested_list.getInfo(), columns=['date', str(Province) + ' Province'])
  NTL_dataset['date'] = pd.to_datetime(NTL_dataset['date'])
  NTL_dataset = NTL_dataset.set_index('date')
  NTL_dataset.insert(1, str(reg_names[reg_codes.index(Region)]), regional_NTL)

  # plt.plot(NTL_dataset)
  # plt.show()
  return NTL_dataset

# NTL_dataset_initialize('Leyte',2364)

NTL_dataset = NTL_dataset_initialize('Leyte',2364)
NTL_dataset

# def NTL_dataset_initialize(Province, Region):

#   viirs = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG').filter(ee.Filter.date('2014-01-01', '2023-12-01'))
#   province = ee.Feature(ee.FeatureCollection("FAO/GAUL/2015/level2").filter(ee.Filter.eq('ADM2_NAME', Province)).first()).geometry()
#   region = ee.Feature(ee.FeatureCollection("FAO/GAUL/2015/level1").filter(ee.Filter.eq('ADM1_CODE', Region)).first()).geometry()

#   def get_prov_mean(img):
#       # img = img.updateMask(img.gte(0))
#       mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=province, scale=500, maxPixels=1e9).get('avg_rad')
#       return img.set('date', img.date().format()).set('mean',mean)

#   def get_reg_mean(img):
#       # img = img.updateMask(img.gte(0))
#       mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=region, scale=500, maxPixels=1e9).get('avg_rad')
#       return img.set('date', img.date().format()).set('mean',mean)

#   # reduce collection
#   prov_mean = viirs.map(get_prov_mean)
#   reg_mean = viirs.map(get_reg_mean)

#   # get lists
#   nested_list = prov_mean.reduceColumns(ee.Reducer.toList(2), ['date','mean']).values().get(0)
#   nested_list_2 = reg_mean.reduceColumns(ee.Reducer.toList(1), ['mean']).values().get(0)
#   regional_NTL = np.asarray(nested_list_2.getInfo()).squeeze()

#   # convert to dataframe
#   NTL_dataset = pd.DataFrame(nested_list.getInfo(), columns=['date', str(Province) + ' Province'])
#   NTL_dataset['date'] = pd.to_datetime(NTL_dataset['date'])
#   NTL_dataset = NTL_dataset.set_index('date')
#   NTL_dataset.insert(1, str(reg_names[reg_codes.index(Region)]), regional_NTL)

#   plt.plot(NTL_dataset)
#   plt.show()
#   return NTL_dataset

	Leyte Province	Region VIII
date
2014-01-01	0.101630	0.087372
2014-02-01	0.170149	0.114812
2014-03-01	0.138900	0.098264
2014-04-01	0.217183	0.181112
2014-05-01	0.172025	0.111598
...	...	...
2023-05-01	0.701959	0.529153
2023-06-01	0.618948	0.461296
2023-07-01	0.148886	0.147419
2023-08-01	0.458526	0.387465
2023-09-01	0.587854	0.433991

117 rows × 2 columns

print(NTL_dataset.index)

DatetimeIndex(['2014-01-01', '2014-02-01', '2014-03-01', '2014-04-01',
               '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
               '2014-09-01', '2014-10-01',
               ...
               '2022-12-01', '2023-01-01', '2023-02-01', '2023-03-01',
               '2023-04-01', '2023-05-01', '2023-06-01', '2023-07-01',
               '2023-08-01', '2023-09-01'],
              dtype='datetime64[ns]', name='date', length=117, freq=None)

STL_extraction(province, code, period)

def STL_extraction(province, code, period):

    region = str(reg_names[reg_codes.index(code)])

    print("Processing NTL extraction for resettlements in " + str(province) + " Province, " + str(region))
    NTL_dataset = NTL_dataset_initialize(province, code)

    prov_df = df.loc[df['Province']==province,:]
    prov_df = prov_df.reset_index(drop=True)
    prov_df.index = prov_df.index + 1


    for i in tqdm(range(1,len(prov_df)+1), desc = region + ": Processing"):
      NTL_data = time_series_(prov_df['Latitude'][i], prov_df['Longitude'][i], prov_df['Buffer'][i])
      NTL_data.index = pd.to_datetime(NTL_data.index)
      idx = pd.date_range(start="2014-01-01",end="2023-09-01", freq='MS')
      NTL_data = NTL_data.reindex(idx)
      NTL_data = NTL_data.interpolate(limit_direction='both')
      NTL_dataset.insert(i+1, prov_df['Project Name'][i], NTL_data)

    trend = NTL_dataset.copy()
    seasonality = NTL_dataset.copy()
    residual = NTL_dataset.copy()

    for loc in tqdm(NTL_dataset.columns, desc = 'STL Decomposition'):
      data = NTL_dataset[loc].values
      decomposition = sm.tsa.seasonal_decompose(data, model='additive', period = period)
      trend[loc] = decomposition.trend
      seasonality[loc] = decomposition.seasonal
      residual[loc] = decomposition.resid

    dataframe = pd.concat([NTL_dataset,trend, seasonality, residual], axis = 1, keys = ['Raw', 'Trend', 'Seasonality', 'Residual'])
    return dataframe

# STL_extraction(province = 'Laguna', code = 67166, period = 12)

Sample Runs (for Debugging)

dataframe = STL_extraction(province = 'Leyte', code = 2364, period = 12)

dataframe

Processing NTL extraction for resettlements in Leyte Province, Region VIII

Region VIII: Processing: 100%|██████████| 38/38 [00:15<00:00,  2.46it/s]
STL Decomposition: 100%|██████████| 40/40 [00:00<00:00, 766.40it/s]

	Raw										...	Residual
	Leyte Province	Region VIII	Kawayan Ville	Villa Diana	Ridge View Park	New Hope Village	Knightsridge Residences	Villa Sofia	North Hill Arbours	Salvacion Heights	...	Villa Soledad	Virlyn Heights Subdivision	Tabango Ville	Dulag Town Ville	Barugo Town Ville	Villa Verde	Capoocan Village	Dagami Town Ville	Sta. Fe Village Site 1	Cool Spring Residences
date
2014-01-01	0.101630	0.087372	0.262384	0.225237	0.240081	0.259939	0.250861	0.207366	0.278039	0.290849	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-02-01	0.170149	0.114812	0.111189	0.107876	0.143410	0.163447	0.108903	0.098870	0.126072	0.084186	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-03-01	0.138900	0.098264	0.059881	0.080907	0.007060	0.065777	0.054506	0.092130	0.044625	0.062283	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-04-01	0.217183	0.181112	0.090566	0.053938	0.099713	0.043851	0.078866	0.064774	0.029750	0.394358	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-05-01	0.172025	0.111598	0.121251	0.026969	0.047443	0.021926	0.066565	0.032387	0.014875	0.102708	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-05-01	0.701959	0.529153	0.648243	0.811948	0.591814	0.431255	0.643906	0.574933	0.732504	0.581307	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-06-01	0.618948	0.461296	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-07-01	0.148886	0.147419	1.413277	1.359351	1.327103	1.486052	1.391000	1.296726	1.438504	1.409091	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-08-01	0.458526	0.387465	0.387297	0.435325	0.426902	0.581734	0.462375	0.452197	0.566908	0.407273	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-09-01	0.587854	0.433991	0.370541	0.110000	0.237657	0.380923	0.214219	0.328789	0.330571	0.257216	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

117 rows × 160 columns

province = 'Leyte'

prov_df = df.loc[df['Province']==province,:]
prov_df = prov_df.reset_index(drop=True)
prov_df.index = prov_df.index + 1
prov_df

	index	Typhoon	Province	Project Name	Location	Latitude	Longitude	Buffer	Classification	Full Project Title
1	24	Yolanda	Leyte	Kawayan Ville	Brgy, Kawayan, Tacloban City	11.323226	124.954521	230.0	Identified	Kawayan Ville I and II
2	25	Yolanda	Leyte	Villa Diana	Brgy. New Kawayan, Tacloban City, Leyte	11.331256	124.953063	120.0	Identified	Villa Diana
3	26	Yolanda	Leyte	Ridge View Park	Brgy. Cabalawan, Tacloban City, Leyte	11.301019	124.952880	270.0	Identified	Ridge View Park 1
4	27	Yolanda	Leyte	New Hope Village	Brgy. New Kawayan & Brgy. Sta. Elena, Tacloban...	11.341332	124.944084	220.0	Identified	New Hope Village
5	28	Yolanda	Leyte	Knightsridge Residences	Brgy. Camansihay, Tacloban City, Leyte	11.304363	124.940941	240.0	Probable	Knightsridge Residences
6	29	Yolanda	Leyte	Villa Sofia	Brgy. Tagpuro, Tacloban City, Leyte	11.343973	124.966135	200.0	Identified	Villa Sofia
7	30	Yolanda	Leyte	North Hill Arbours	Brgy. Sto. Niño (Suhi), Tacloban City	11.323519	124.947365	330.0	Identified	North Hill Arbours
8	31	Yolanda	Leyte	Salvacion Heights	Brgy. Salvacion (Tigbao), Tacloban City, Leyte	11.226967	124.952686	180.0	Identified	Salvacion Heights
9	32	Yolanda	Leyte	Guadalupe Heights	Brgy. San Isidro (Suhi), Tacloban City, Leyte	11.318076	124.944458	500.0	Identified	Guadalupe Heights 1, 2 and 3 with Greendale Re...
10	33	Yolanda	Leyte	St. Francis Village	Brgy. Suhi, Tacloban City	11.319448	124.956916	230.0	Probable	St. Francis Village
11	34	Yolanda	Leyte	Pope Francis Village	Brgy. 99, Diit, Tacloban City	11.260468	124.961335	240.0	Identified	Pope Francis Village
12	35	Yolanda	Leyte	Kapuso Village Project Palo	Brgy. San Jose, Palo, Leyte	11.173572	124.980725	100.0	Identified	Kapuso Village Project Palo
13	36	Yolanda	Leyte	Ver Dominic Heights	Brgy. San Jose, Palo Site 1, Leyte	11.175870	124.984272	190.0	Probable	Ver Dominic Heights
14	37	Yolanda	Leyte	Prime Town Phase 1	Brgy. Pago, Tanauan, Leyte	11.101975	125.001334	120.0	Identified	Prime Town Phase 1
15	38	Yolanda	Leyte	Prime Town Phase 3	Brgy. Sacme, Tanauan, Leyte	11.070821	125.018836	210.0	Identified	Prime Town Phase 3
16	39	Yolanda	Leyte	Channel Ridge View	Brgy. Poblacion, Babatngon, Leyte	11.419122	124.840478	350.0	Identified	Channel Ridge View
17	40	Yolanda	Leyte	Pastrana Ville	Brgy. Aringit, Pastrana, Leyte	11.117005	124.882018	300.0	Identified	Pastrana Ville
18	41	Yolanda	Leyte	Carigara Housing Project	Brgy. Parag-um, Carigara, Leyte	11.264629	124.726101	200.0	Probable	Carigara Housing Project
19	42	Yolanda	Leyte	Carigara Housing Project 1	Brgy. Sagkahan, Carigara, Leyte	11.281128	124.728201	170.0	Identified	Carigara Housing Project 1
20	43	Yolanda	Leyte	Ecoville	Brgy. Sagkahan, Carigara, Leyte	11.280059	124.721912	300.0	Identified	Ecoville 1&2
21	44	Yolanda	Leyte	Vergabriel's Heights Subdivision	Brgy. Parag-um, Carigara, Leyte	11.264581	124.726034	200.0	Identified	Vergabriel's Heights Subdivision
22	45	Yolanda	Leyte	Mayorga Towne Homes	Brgy. A. Bonifacio, Mayorga, Leyte	10.900203	124.975367	120.0	Probable	Mayorga Towne Homes
23	46	Yolanda	Leyte	Paglaum Ville	Brgy. San Isidro, McArthur, Leyte	10.829067	124.992899	90.0	Probable	Paglaum Ville
24	47	Yolanda	Leyte	Mont Eagle Ville IV	Brgy. Cangmoya Site 1, Palompon, Leyte	10.988231	124.429969	280.0	Identified	Mont Eagle Ville IV Subdivision Site 1
25	48	Yolanda	Leyte	Tolosa Haiyan Village	Brgy. Tanghas, Tolosa, Leyte	11.052954	125.031086	200.0	Probable	Tolosa Haiyan Village & St. Michael Residences
26	49	Yolanda	Leyte	Matlang Heights	Matlang, Isabel, Leyte	10.894580	124.456139	250.0	Identified	Matlang Heights 1-2
27	50	Yolanda	Leyte	Coconut Grove Subdivision	Brgy. Dita, Julita, Leyte	10.972429	124.948448	130.0	Probable	Coconut Grove Subdivision
28	51	Yolanda	Leyte	Bagong Pag-Asa Subdivision	Brgy. Margen Site 1 (and 2), Ormoc City, Leyte	11.021036	124.530845	300.0	Probable	Bagong Pag-Asa Subdivision Phase 1 and 2
29	52	Yolanda	Leyte	Villa Soledad	Brgy. Punta and Jaena, Baybay City, Leyte	10.635620	124.782669	200.0	Identified	Villa Soledad
30	53	Yolanda	Leyte	Virlyn Heights Subdivision	Brgy. Sambel, Barauen, Leyte	10.947098	124.907274	120.0	Probable	Virlyn Heights Subdivision
31	54	Yolanda	Leyte	Tabango Ville	Brgy. Manlawaan, Tabango, Leyte	11.333094	124.423100	100.0	Probable	Tabango Ville
32	55	Yolanda	Leyte	Dulag Town Ville	Brgy. San Antonio, Dulag, Leyte	10.950564	125.027621	110.0	Probable	Dulag Town Ville
33	56	Yolanda	Leyte	Barugo Town Ville	Brgy. Busay, Barugo, Leyte	11.324979	124.770885	190.0	Probable	Barugo Town Ville Phase 1 & 2
34	57	Yolanda	Leyte	Villa Verde	Brgy. Iligay, Villaba, Leyte	11.253827	124.447524	180.0	Probable	Villa Verde
35	58	Yolanda	Leyte	Capoocan Village	Brgy. Balucanad, Capoocan, Leyte	11.281677	124.646016	200.0	Identified	Capoocan Village
36	59	Yolanda	Leyte	Dagami Town Ville	Brgy. Sawahon, Dagami, Leyte	11.070834	124.876392	120.0	Identified	Dagami Town Ville
37	60	Yolanda	Leyte	Sta. Fe Village Site 1	Brgy. Bulod, Sta. Fe, Leyte	11.173139	124.924521	130.0	Probable	Sta. Fe Village Site 1
38	61	Yolanda	Leyte	Cool Spring Residences	Brgy. Arado, Burauen, Leyte	10.979356	124.920860	120.0	Probable	Cool Spring Residences (Burauen Housing Project)

# code = 2364
# period = 12
# region = str(reg_names[reg_codes.index(code)])

# for i in tqdm(range(1,len(prov_df[:1])+1), desc = region + ": Processing"):
#       NTL_data = time_series_(prov_df['Latitude'][i], prov_df['Longitude'][i], prov_df['Buffer'][i])
#       NTL_data.index = pd.to_datetime(NTL_data.index)
#       idx = pd.date_range(start="2014-01-01",end="2023-09-01", freq='MS')
#       NTL_data = NTL_data.reindex(idx)
# NTL_data

# NTL_data.index = pd.to_datetime(NTL_data.index)

# print(pd.date_range(start="2014-01-01",end="2023-09-01", freq='MS').difference(NTL_data.index))

province = 'Leyte'
code = 2364
period = 12
region = str(reg_names[reg_codes.index(code)])

# fig, ax = plt.subplots(nrows=4,ncols=1, sharex='col',
#                   gridspec_kw={'height_ratios':[0.75,1,0.5,0.5]},
#                   figsize=(10,5), dpi = 200)

import matplotlib
matplotlib.rcParams.update(matplotlib.rcParamsDefault)

fig, ax = plt.subplots(nrows=4,ncols=1, sharex='col',
                      gridspec_kw={'height_ratios':[0.75,2,0.75,0.5]},
                      figsize=(14,6), dpi = 200)


# matplotlib.rcParams['axes.edgecolor'] = 'white'
# plt.style.context('dark_background')
# plt.style.use('dark_background')
# plt.rcParams.update({
#     "lines.color": "white",
#     "patch.edgecolor": "white",
#     "text.color": "black",
#     "axes.facecolor": "white",
#     "axes.edgecolor": "lightgray",
#     "axes.labelcolor": "white",
#     "xtick.color": "white",
#     "ytick.color": "white",
#     "grid.color": "lightgray",
#     "figure.facecolor": "black",
#     "figure.edgecolor": "black",
#     "savefig.facecolor": "black",
#     "savefig.edgecolor": "black"})

# fig.patch.set_facecolor('white')
# fig.patch.set_alpha(0)
# ax.xaxis.label.set_color('white')        #setting up X-axis label color to yellow
# ax.yaxis.label.set_color('white')          #setting up Y-axis label color to blue
# ax.tick_params(axis='x', colors='white')    #setting up X-axis tick color to red
# ax.tick_params(axis='y', colors='white')  #setting up Y-axis tick color to black


date = dataframe.index
trend = dataframe['Trend']
seasonality = dataframe['Seasonality']
residual = dataframe['Residual']

prov_df = df.loc[df['Province']==province,:]

for i in tqdm(range(len(prov_df)), desc = 'Processing: '):

  data = dataframe['Raw'].iloc[:,i+2].values
  line_color = plt.cm.Spectral(i/len(prov_df))

  ax[0].plot(date, data, color = line_color, linewidth = 1, alpha = 0.8)
  ax[0].set_ylabel('$nW ·cm^{−2}·sr^{−1}$', color = 'white')

  ax[1].plot(date, trend.iloc[:,i+2], color = line_color,
            alpha = 0.8, label= dataframe['Raw'].columns[i+2])
  ax[1].set_ylabel('trend')

  ax[2].plot(date, seasonality.iloc[:,i+2], color = line_color,
            linewidth = 1, alpha = 0.6)
  ax[2].set_ylabel('seasonality')

  resid = residual.iloc[:,i+2]
  ax[3].bar(date, resid, width = 10, color = line_color, alpha = 0.8)
  ax[3].set_ylabel('residual')


ax[1].legend(fontsize=8, ncol = 5, bbox_to_anchor= (0, 1.05, 1, 0.5), loc="lower left",
            bbox_transform=ax[0].transAxes, fancybox = True, mode='expand')

ax[0].plot(dataframe['Raw'].iloc[:,0], 'b-', drawstyle = 'steps-mid',  linewidth = 1, alpha = 0.5)
ax[0].plot(dataframe['Raw'].iloc[:,1], 'k-', drawstyle = 'steps-mid', linewidth = 1, alpha = 0.5)
ax[1].plot(trend.iloc[:,0], 'b.-', drawstyle = 'steps-mid', label= str(province) + ' Province',  linewidth = 3, alpha = 0.8)
ax[1].plot(trend.iloc[:,1], 'k.-', drawstyle = 'steps-mid', label= str(region), linewidth = 2, alpha = 0.8)
ax[2].plot(seasonality.iloc[:,0], 'b-', drawstyle = 'steps-mid',  linewidth = 1.5, alpha = 0.8)
ax[2].plot(seasonality.iloc[:,1], 'k-', drawstyle = 'steps-mid', linewidth = 1, alpha = 0.8)
ax[3].bar(date, residual.iloc[:,0], color = 'b',  width = 15, alpha = 0.8)
ax[3].bar(date, residual.iloc[:,1], color = 'k', width = 12,  alpha = 0.8)

for ax in ax:
  ax.grid(alpha = 0.5)
  ax.axvline(datetime.date(2020, 3, 10), color = 'r', lw = 5, alpha = 0.2) #For COVID lockdown
  ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f')) # 2 decimal places)
  ax.minorticks_on()
  ax.xaxis.grid(True, which='minor', alpha = 0.2)


dtFmt = mdates.DateFormatter('%Y') # define the formatting
plt.gca().xaxis.set_major_formatter(dtFmt) # apply the format to the desired axis
plt.tight_layout()
# plt.savefig(results+"/CH6-TS-"+str(province)+".png", bbox_inches="tight")
plt.show()

Processing: 100%|██████████| 38/38 [00:06<00:00,  6.22it/s]

dataframe.index

DatetimeIndex(['2014-01-01', '2014-02-01', '2014-03-01', '2014-04-01',
               '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
               '2014-09-01', '2014-10-01',
               ...
               '2022-12-01', '2023-01-01', '2023-02-01', '2023-03-01',
               '2023-04-01', '2023-05-01', '2023-06-01', '2023-07-01',
               '2023-08-01', '2023-09-01'],
              dtype='datetime64[ns]', name='date', length=117, freq=None)

trend

	Leyte Province	Region VIII	Kawayan Ville	Villa Diana	Ridge View Park	New Hope Village	Knightsridge Residences	Villa Sofia	North Hill Arbours	Salvacion Heights	...	Villa Soledad	Virlyn Heights Subdivision	Tabango Ville	Dulag Town Ville	Barugo Town Ville	Villa Verde	Capoocan Village	Dagami Town Ville	Sta. Fe Village Site 1	Cool Spring Residences
date
2014-01-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-02-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-03-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-04-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2014-05-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-05-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-06-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-07-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-08-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-09-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

117 rows × 40 columns

# y = trend['Leyte Province'].dropna().values
y = trend.dropna().values
x = np.arange(len(y))
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

slope, intercept, r_value, p_value, std_err

fig, ax = plt.subplots(nrows=2,ncols=1,
                       sharex='col', gridspec_kw={'height_ratios':[0.75,0.25]},
                       figsize=(15,6), dpi = 200)

ax[0].grid(alpha = 0.3)
# ax.plot(dataframe['Raw'].iloc[:,0]-0.01, 'k.-', drawstyle = 'steps-mid',  linewidth = 2)
ax[0].plot(dataframe['Raw'].iloc[:,0], 'k.-', drawstyle = 'steps-mid',  linewidth = 1, label = 'NTL average radiance ($y_t$)', alpha = 0.5)
trend = dataframe['Trend'].iloc[:,0]
ax[0].plot(trend, 'b.-', alpha = 1, ms = 15, lw = 5, label = 'NTL Trend ($T_t$)')

ax[0].plot(dataframe.index[6:-6], intercept + slope*x, 'r--', label = 'Best Fit Line on NTL Trend (ROC)', lw = 10, alpha = 0.8)


# ax[0].plot([date[6], date[-7]], [trend[6], trend[-7]], 'k--', label = 'Rate of Change', lw = 3)
# ax[0].plot([date[6], date[-7]], [trend[6], trend[-7]], 'yo')
# ax[0].legend(fontsize=7, ncol = 5, bbox_to_anchor= (0, 1.0, 1, 0.5), loc="lower left",
#             bbox_transform=ax[0].transAxes, fancybox = True, mode='expand')

ax[0].legend(title = 'Leyte Province',fancybox = True)

ax[0].annotate('net change: +'+ str(np.round(slope*107, 3)), xy = (date[84],0.04), xytext=(date[84], 0.04))
ax[0].annotate('slope: +'+ str(np.round(slope*12, 3)) + ' per year', xy = (date[84],0.0), xytext=(date[84], 0.0))
ax[0].annotate('intercept: '+ str(np.round(intercept, 3)), xy = (date[84],-0.04), xytext=(date[84], -0.04))
ax[0].annotate('r-value: ' + str(np.round(r_value, 5)), xy = (date[84],-0.08), xytext=(date[84], -0.08))
ax[0].set_ylabel('$nW ·cm^{−2}·sr^{−1}$')

ax[1].grid(alpha = 0.3)
ax[1].plot(dataframe['Seasonality'].iloc[:,0], color = 'g',
           ls = '-', drawstyle = 'steps-mid', alpha = 0.75, lw = 5, label = 'NTL Seasonality ($S_t$)')
ax[1].bar(date, dataframe['Residual'].iloc[:,0], width = 20, color = 'orange',
          alpha = 1, label = 'Residual ($E_t$)')
# ax[1].set_xticks([date[0], date[27], date[54], date[-28], date[-1]])
ax[1].legend(ncol = 2, fancybox = True)
ax[1].set_ylabel('$nW ·cm^{−2}·sr^{−1}$')

dtFmt = mdates.DateFormatter('%Y') # define the formatting
plt.gca().xaxis.set_major_formatter(dtFmt) # apply the format to the desired axis
plt.tight_layout()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-73-b47c53996ada> in <cell line: 4>()
      2 y = trend.dropna().values
      3 x = np.arange(len(y))
----> 4 slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
      5 
      6 slope, intercept, r_value, p_value, std_err

/usr/local/lib/python3.10/dist-packages/scipy/stats/_stats_mstats_common.py in linregress(x, y, alternative)
    165     #   ssxm = mean( (x-mean(x))^2 )
    166     #   ssxym = mean( (x-mean(x)) * (y-mean(y)) )
--> 167     ssxm, ssxym, _, ssym = np.cov(x, y, bias=1).flat
    168 
    169     # R-value

/usr/local/lib/python3.10/dist-packages/numpy/core/overrides.py in cov(*args, **kwargs)

/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py in cov(m, y, rowvar, bias, ddof, fweights, aweights, dtype)
   2637         if not rowvar and y.shape[0] != 1:
   2638             y = y.T
-> 2639         X = np.concatenate((X, y), axis=0)
   2640 
   2641     if ddof is None:

/usr/local/lib/python3.10/dist-packages/numpy/core/overrides.py in concatenate(*args, **kwargs)

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 93 and the array at index 1 has size 40

NTL plotter

def timeseries_resettlements(province, code, period):

    region = str(reg_names[reg_codes.index(code)])

    print("Processing NTL extraction for resettlements in " + str(province) + " Province, " + str(region))
    NTL_dataset = NTL_dataset_initialize(province, code)
    prov_df = df.loc[df['Province']==province,:]
    prov_df = prov_df.reset_index(drop=True)
    prov_df.index = prov_df.index + 1

    for i in tqdm(range(1,len(prov_df)+1), desc = region + ": Processing"):
      # NTL_data = time_series_(prov_df['Latitude'][i], prov_df['Longitude'][i], prov_df['Buffer'][i])
      # NTL_dataset.insert(i+1, prov_df['Project Name'][i], NTL_data)
      NTL_data = time_series_(prov_df['Latitude'][i], prov_df['Longitude'][i], prov_df['Buffer'][i])
      NTL_data.index = pd.to_datetime(NTL_data.index)
      idx = pd.date_range(start="2014-01-01",end="2023-09-01", freq='MS')
      NTL_data = NTL_data.reindex(idx)
      NTL_data = NTL_data.interpolate(limit_direction='both')
      NTL_dataset.insert(i+1, prov_df['Project Name'][i], NTL_data)


    trend = NTL_dataset.copy()
    seasonality = NTL_dataset.copy()
    residual = NTL_dataset.copy()

    for loc in tqdm(NTL_dataset.columns, desc = 'STL Decomposition'):
      data = NTL_dataset[loc].values
      decomposition = sm.tsa.seasonal_decompose(data, model='additive', period = period)
      trend[loc] = decomposition.trend
      seasonality[loc] = decomposition.seasonal
      residual[loc] = decomposition.resid

    print("Plotting timeseries NTL data")

    fig, ax = plt.subplots(nrows=4,ncols=1, sharex='col',
                      gridspec_kw={'height_ratios':[0.75,2,0.75,0.5]},
                      figsize=(15,5), dpi = 200)

    date = NTL_dataset.index

    NTL_dataset.copy()

    ax[0].plot(NTL_dataset.iloc[:,0], 'b.-', drawstyle = 'steps-mid',  linewidth = 3, alpha = 0.5)
    ax[0].plot(NTL_dataset.iloc[:,1], 'k.-', drawstyle = 'steps-mid', linewidth = 2, alpha = 0.5)

    ax[1].plot(trend.iloc[:,0], 'b.-', drawstyle = 'steps-mid', label= str(province) + ' Province',  linewidth = 3, alpha = 0.8)
    ax[1].plot(trend.iloc[:,1], 'k.-', drawstyle = 'steps-mid', label= str(region), linewidth = 2, alpha = 0.8)
    ax[2].plot(seasonality.iloc[:,0], 'b-', drawstyle = 'steps-mid',  linewidth = 1.5, alpha = 0.8)
    ax[2].plot(seasonality.iloc[:,1], 'k-', drawstyle = 'steps-mid', linewidth = 1, alpha = 0.8)
    ax[3].bar(date, residual.iloc[:,0], color = 'b',  width = 15, alpha = 0.8)
    ax[3].bar(date, residual.iloc[:,1], color = 'k', width = 12,  alpha = 0.8)

    for i in tqdm(range(len(prov_df)), desc = 'Processing: '):

      data = NTL_dataset.iloc[:,i+2].values
      line_color = plt.cm.Spectral(i/len(prov_df))

      ax[0].plot(date, data, color = line_color, linewidth = 1, alpha = 0.8)
      ax[0].set_ylabel('$nW ·cm^{−2}·sr^{−1}$')

      ax[1].plot(date, trend.iloc[:,i+2], color = line_color,
                linewidth = prov_df['Buffer'][i+1]/100, alpha = 0.8, label= NTL_dataset.columns[i+2])
      ax[1].set_ylabel('trend')

      ax[2].plot(date, seasonality.iloc[:,i+2], color = line_color,
                linewidth = 1, alpha = 0.6)
      ax[2].set_ylabel('seasonality')

      resid = residual.iloc[:,i+2]
      ax[3].bar(date, resid, width = 10, color = line_color, alpha = 0.8)
      ax[3].set_ylabel('residual')


    ax[1].legend(fontsize=8, ncol = 5, bbox_to_anchor= (0, 1.05, 1, 0.5), loc="lower left",
                bbox_transform=ax[0].transAxes, fancybox = True, mode='expand')

    for ax in ax:
      ax.grid(alpha = 0.5)
      ax.axvline(datetime.date(2020, 3, 10), color = 'r', lw = 5, alpha = 0.2) #For COVID lockdown
      ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f')) # 2 decimal places)
      ax.minorticks_on()
      ax.xaxis.grid(True, which='minor', alpha = 0.2)



    dtFmt = mdates.DateFormatter('%b-%Y') # define the formatting
    plt.gca().xaxis.set_major_formatter(dtFmt) # apply the format to the desired axis
    # plt.tight_layout()
    # plt.savefig(results+"/CH6-TS-"+str(province)+".png", bbox_inches="tight")
    plt.show()

    return NTL_dataset, trend, seasonality, residual

NTL_dataset, trend, seasonality, residual = timeseries_resettlements(province = 'Leyte', code = 2364, period = 12)

Processing NTL extraction for resettlements in Leyte Province, Region VIII

Region VIII: Processing: 100%|██████████| 38/38 [00:15<00:00,  2.53it/s]
STL Decomposition: 100%|██████████| 40/40 [00:00<00:00, 468.62it/s]

Plotting timeseries NTL data

Processing: 100%|██████████| 38/38 [00:08<00:00,  4.68it/s]

dataset = dataframe['Trend'].dropna()

stats_summary = pd.DataFrame(index = dataset.columns,
                             columns = ['slope', 'intercept', 'r', 'p', 'std'])

plt.figure(dpi = 150, figsize = (10,5))
plt.grid(alpha = 0.5)

for i in tqdm(range(len(dataset.columns))):
  loc = dataset.columns[i]
  y = dataset[loc]
  x = np.arange(len(y))
  slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
  stats_summary.loc[loc] = np.asarray(stats.linregress(x,y))

  line_color = plt.cm.jet(i/len(dataset.columns))
  if i > 1:
    # plt.plot(dataset.index, y, '.-', color = line_color, alpha = 0.1)
    plt.plot(dataset.index, intercept + slope*x, color = line_color, label=str(loc),  alpha = 0.5)
  else:
    plt.plot(dataset.index, y, '.', color = plt.cm.magma(i), alpha = 0.1)
    plt.plot(dataset.index, intercept + slope*x, color = plt.cm.RdPu((i+1)/2), lw = 5, label=str(loc), linestyle = '--')
  # if i%5 == 0:
    # plt.plot(dataset.index, y, '.', color = line_color, alpha = 0.5)
    # plt.plot(dataset.index, intercept + slope*x, color = line_color, label=str(loc))


plt.legend(fontsize=6, ncol = 5, bbox_to_anchor= (0, 1.05, 1, 0.5), loc="lower left",
              bbox_transform=plt.gca().transAxes, fancybox = True, mode='expand')
# plt.legend()
plt.show()

100%|██████████| 40/40 [00:00<00:00, 614.07it/s]

Saving DF and stats

results_path = '/content/gdrive/MyDrive/Research - PhD/Results/Resettlements/'

os.listdir(results_path)

['NTL STL', 'trend stats']

stats_summary(province, code, period)

def stats_summary(province, code, period):
    dataframe = STL_extraction(province, code, period)
    dataset = dataframe['Trend'].dropna()
    dataframe.to_csv(results_path + "NTL STL/" +  str(province) + '_df.csv')

    stats_summary = pd.DataFrame(index = dataset.columns,
                                columns = ['slope', 'intercept', 'r', 'p', 'std'])

    # plt.figure(dpi = 150, figsize = (10,5))
    # plt.grid(alpha = 0.5)

    for i in tqdm(range(len(dataset.columns))):
      loc = dataset.columns[i]
      y = dataset[loc]
      x = np.arange(len(y))
      slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
      stats_summary.loc[loc] = np.asarray(stats.linregress(x,y))

    #   line_color = plt.cm.jet(i/len(dataset.columns))
    #   if i > 1:
    #     # plt.plot(dataset.index, y, '.-', color = line_color, alpha = 0.1)
    #     plt.plot(dataset.index, intercept + slope*x, color = line_color, label=str(loc),  alpha = 0.5)
    #   else:
    #     plt.plot(dataset.index, y, '.', color = plt.cm.RdPu((i+1)/2), alpha = 0.1)
    #     plt.plot(dataset.index, intercept + slope*x, color = plt.cm.RdPu((i+1)/2), lw = 5, label=str(loc), linestyle = '--')
    #   # if i%5 == 0:
    #     # plt.plot(dataset.index, y, '.', color = line_color, alpha = 0.5)
    #     # plt.plot(dataset.index, intercept + slope*x, color = line_color, label=str(loc))


    # plt.legend(fontsize=6, ncol = 5, bbox_to_anchor= (0, 1.05, 1, 0.5), loc="lower left",
    #               bbox_transform=plt.gca().transAxes, fancybox = True, mode='expand')
    # # plt.legend()
    # plt.show()

    # stats_summary.to_csv(results_path + "trend stats/" + str(province) + '_stats.csv')

    return dataframe, stats_summary

Leyte

Leyte_df, Leyte_stats = stats_summary(province = 'Leyte', code = 2364, period = 12)

Processing NTL extraction for resettlements in Leyte Province, Region VIII

Region VIII: Processing: 100%|██████████| 38/38 [00:16<00:00,  2.28it/s]
STL Decomposition: 100%|██████████| 40/40 [00:00<00:00, 539.06it/s]
100%|██████████| 40/40 [00:00<00:00, 1229.79it/s]

Cebu

Cebu_df, Cebu_stats = stats_summary(province = 'Cebu', code = 2363, period = 12)

Processing NTL extraction for resettlements in Cebu Province, Region VII

Region VII: Processing: 100%|██████████| 23/23 [00:18<00:00,  1.23it/s]
STL Decomposition: 100%|██████████| 25/25 [00:00<00:00, 677.76it/s]
100%|██████████| 25/25 [00:00<00:00, 1960.54it/s]

Samar

Samar_df, Samar_stats = stats_summary(province = 'Samar', code = 2364, period = 12)

Processing NTL extraction for resettlements in Samar Province, Region VIII

Region VIII: Processing: 100%|██████████| 19/19 [00:15<00:00,  1.19it/s]
STL Decomposition: 100%|██████████| 21/21 [00:00<00:00, 710.92it/s]
100%|██████████| 21/21 [00:00<00:00, 1574.64it/s]

Biliran

Biliran_df, Biliran_stats = stats_summary(province = 'Biliran', code = 2364, period = 12)

Processing NTL extraction for resettlements in Biliran Province, Region VIII

Region VIII: Processing: 100%|██████████| 12/12 [00:10<00:00,  1.18it/s]
STL Decomposition: 100%|██████████| 14/14 [00:00<00:00, 155.39it/s]
100%|██████████| 14/14 [00:00<00:00, 375.20it/s]

Aklan

Aklan_df, Aklan_stats = stats_summary(province = 'Aklan', code = 2362, period = 12)

Processing NTL extraction for resettlements in Aklan Province, Region VI

Region VI: Processing: 100%|██████████| 12/12 [00:13<00:00,  1.09s/it]
STL Decomposition: 100%|██████████| 14/14 [00:00<00:00, 651.78it/s]
100%|██████████| 14/14 [00:00<00:00, 1079.95it/s]

Antique

Antique_df, Antique_stats = stats_summary(province = 'Antique', code = 2362, period = 12)

Processing NTL extraction for resettlements in Antique Province, Region VI

Region VI: Processing: 100%|██████████| 26/26 [00:22<00:00,  1.17it/s]
STL Decomposition: 100%|██████████| 28/28 [00:00<00:00, 396.33it/s]
100%|██████████| 28/28 [00:00<00:00, 1143.95it/s]

MisOr

MisOr_df, MisOr_stats = stats_summary(province = 'Misamis Oriental', code = 67160, period = 12)

Processing NTL extraction for resettlements in Misamis Oriental Province, Region X

Region X: Processing: 100%|██████████| 16/16 [00:12<00:00,  1.25it/s]
STL Decomposition: 100%|██████████| 18/18 [00:00<00:00, 677.20it/s]
100%|██████████| 18/18 [00:00<00:00, 1041.65it/s]

Lanao

Lanao_df, Lanao_stats = stats_summary(province = 'Lanao Del Norte', code = 67160, period = 12)

Processing NTL extraction for resettlements in Lanao Del Norte Province, Region X

Region X: Processing: 100%|██████████| 7/7 [00:05<00:00,  1.22it/s]
STL Decomposition: 100%|██████████| 9/9 [00:00<00:00, 137.68it/s]
100%|██████████| 9/9 [00:00<00:00, 785.04it/s]

Albay

Albay_df, Albay_stats = stats_summary(province = 'Albay', code = 2361, period = 12)

Processing NTL extraction for resettlements in Albay Province, Region V

Region V: Processing: 100%|██████████| 14/14 [00:11<00:00,  1.17it/s]
STL Decomposition: 100%|██████████| 16/16 [00:00<00:00, 599.14it/s]
100%|██████████| 16/16 [00:00<00:00, 1116.43it/s]

Catanduanes

Catanduanes_df, Catanduanes_stats = stats_summary(province = 'Catanduanes', code = 2361, period = 12)

Processing NTL extraction for resettlements in Catanduanes Province, Region V

Region V: Processing: 100%|██████████| 2/2 [00:01<00:00,  1.94it/s]
STL Decomposition: 100%|██████████| 4/4 [00:00<00:00, 376.16it/s]
100%|██████████| 4/4 [00:00<00:00, 794.71it/s]

Laguna

Laguna_df, Laguna_stats = stats_summary(province = 'Laguna', code = 67166, period = 12)

Processing NTL extraction for resettlements in Laguna Province, Region IV-A

Region IV-A: Processing: 100%|██████████| 4/4 [00:03<00:00,  1.33it/s]
STL Decomposition: 100%|██████████| 6/6 [00:00<00:00, 581.09it/s]
100%|██████████| 6/6 [00:00<00:00, 1019.11it/s]

Davao

Davao_df, Davao_stats = stats_summary(province = 'Davao Oriental', code = 67161, period = 12)

Processing NTL extraction for resettlements in Davao Oriental Province, Region XI

Region XI: Processing: 100%|██████████| 8/8 [00:07<00:00,  1.11it/s]
STL Decomposition: 100%|██████████| 10/10 [00:00<00:00, 323.13it/s]
100%|██████████| 10/10 [00:00<00:00, 822.86it/s]

Bulacan

Bulacan_df, Bulacan_stats = stats_summary(province = 'Bulacan', code = 67165, period = 12)

Processing NTL extraction for resettlements in Bulacan Province, Region III

Region III: Processing: 100%|██████████| 4/4 [00:03<00:00,  1.19it/s]
STL Decomposition: 100%|██████████| 6/6 [00:00<00:00, 563.38it/s]
100%|██████████| 6/6 [00:00<00:00, 958.22it/s]

Iloilo_df, Iloilo_stats = stats_summary(province = 'Iloilo', code = 2362, period = 12)

Processing NTL extraction for resettlements in Iloilo Province, Region VI

Region VI: Processing: 100%|██████████| 20/20 [00:18<00:00,  1.07it/s]
STL Decomposition: 100%|██████████| 22/22 [00:00<00:00, 680.19it/s]
100%|██████████| 22/22 [00:00<00:00, 1671.95it/s]

Negros_Occidental_df, Negros_Occidental_stats = stats_summary(province = 'Negros Occidental', code = 2362, period = 12)

Processing NTL extraction for resettlements in Negros Occidental Province, Region VI

Region VI: Processing: 0it [00:00, ?it/s]
STL Decomposition: 100%|██████████| 2/2 [00:00<00:00, 385.29it/s]
100%|██████████| 2/2 [00:00<00:00, 735.07it/s]

Rizal

Rizal_df, Rizal_stats = stats_summary(province = 'Rizal', code = 67166, period = 12)

Processing NTL extraction for resettlements in Rizal Province, Region IV-A

Region IV-A: Processing: 100%|██████████| 2/2 [00:00<00:00,  2.28it/s]
STL Decomposition: 100%|██████████| 4/4 [00:00<00:00, 414.14it/s]
100%|██████████| 4/4 [00:00<00:00, 786.33it/s]

Negros_Or_df, Negros_Or_stats = stats_summary(province = 'Negros Oriental', code = 2363, period = 12)

Processing NTL extraction for resettlements in Negros Oriental Province, Region VII

Region VII: Processing: 100%|██████████| 1/1 [00:00<00:00,  2.02it/s]
STL Decomposition: 100%|██████████| 3/3 [00:00<00:00, 488.87it/s]
100%|██████████| 3/3 [00:00<00:00, 552.08it/s]

# NCR_df, NCR_stats = stats_summary(province = 'National Capital Region', code = 2355, period = 12)

# Ilocos_Norte_df, Ilocos_Norte_stats = stats_summary(province = 'Ilocos Norte', code = 2356, period = 12)

Processing NTL extraction for resettlements in Ilocos Norte Province, Region I

Region I: Processing: 0it [00:00, ?it/s]
STL Decomposition: 100%|██████████| 2/2 [00:00<00:00, 508.99it/s]
100%|██████████| 2/2 [00:00<00:00, 647.22it/s]

Benguet_df, Bengue_stats = stats_summary(province = 'Benguet', code = 2354, period = 12)

Processing NTL extraction for resettlements in Benguet Province, CAR

CAR: Processing: 100%|██████████| 1/1 [00:00<00:00,  2.06it/s]
STL Decomposition: 100%|██████████| 3/3 [00:00<00:00, 351.31it/s]
100%|██████████| 3/3 [00:00<00:00, 789.69it/s]

Trend Analysis

resettlement_stats = pd.concat([Leyte_stats.reset_index(drop=True),
                                Samar_stats.reset_index(drop=True),
                                Biliran_stats.reset_index(drop=True),
                                Cebu_stats.reset_index(drop=True),
                                Aklan_stats.reset_index(drop=True),
                                Antique_stats.reset_index(drop=True),
                                MisOr_stats.reset_index(drop=True),
                                Lanao_stats.reset_index(drop=True),
                                Albay_stats.reset_index(drop=True),
                                Catanduanes_stats.reset_index(drop=True),
                                Laguna_stats.reset_index(drop=True),
                                Davao_stats.reset_index(drop=True),
                                Bulacan_stats.reset_index(drop=True),
                                Rizal_stats.reset_index(drop=True)],
                               axis = 1,
                               keys = ['Leyte', 'Samar', 'Biliran', 'Cebu', 'Aklan', 'Antique', 'Mis Or', 'Lanao DN', 'Albay',
                                       'Catanduanes', 'Laguna', 'Davao', 'Bulacan', 'Rizal'],
                               )
resettlement_stats

	Leyte					Samar					...	Bulacan					Rizal
	slope	intercept	r	p	std	slope	intercept	r	p	std	...	slope	intercept	r	p	std	slope	intercept	r	p	std
0	0.003486	0.16042	0.900777	0.0	0.000173	0.003265	0.130459	0.916345	0.0	0.000147	...	0.008516	0.748784	0.949969	0.0	0.000289	0.007295	1.130044	0.97378	0.0	0.000176
1	0.003232	0.060749	0.899108	0.0	0.000162	0.003232	0.060749	0.899108	0.0	0.000162	...	0.004685	0.437237	0.953814	0.0	0.000152	0.005511	0.729565	0.97068	0.0	0.000141
2	0.00292	0.166143	0.742705	0.0	0.000272	0.003852	0.111861	0.675657	0.0	0.000433	...	0.003333	0.163761	0.860448	0.0	0.000204	0.003107	0.156065	0.816606	0.0	0.000226
3	0.002866	0.175993	0.708536	0.0	0.000294	0.003362	0.142182	0.66659	0.0	0.000388	...	0.002821	0.158615	0.846683	0.0	0.000183	0.003723	0.125531	0.886508	0.0	0.0002
4	0.002718	0.191979	0.675709	0.0	0.000306	0.003145	0.12185	0.705686	0.0	0.000326	...	0.004108	0.141188	0.892473	0.0	0.000214	NaN	NaN	NaN	NaN	NaN
5	0.002907	0.206392	0.626332	0.0	0.000373	0.00156	0.220641	0.404609	0.000043	0.000364	...	0.003766	0.131282	0.880035	0.0	0.00021	NaN	NaN	NaN	NaN	NaN
6	0.002496	0.188876	0.653172	0.0	0.000298	0.002409	0.166632	0.562825	0.0	0.000365	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	0.002933	0.167215	0.713369	0.0	0.000297	0.003371	0.120913	0.652759	0.0	0.000403	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	0.003314	0.155804	0.77092	0.0	0.000282	0.003774	0.137719	0.769347	0.0	0.000323	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9	0.002622	0.185688	0.607865	0.0	0.000353	0.001863	0.188459	0.576986	0.0	0.000272	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10	0.003302	0.154291	0.766497	0.0	0.000285	0.002125	0.171147	0.542177	0.0	0.00034	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11	0.003085	0.164648	0.720109	0.0	0.000307	0.002659	0.177516	0.54897	0.0	0.000418	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
12	0.002618	0.209615	0.632839	0.0	0.00033	0.003413	0.142952	0.62642	0.0	0.000438	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
13	0.003552	0.12851	0.785184	0.0	0.000289	0.001957	0.2037	0.586661	0.0	0.000279	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
14	0.003505	0.12317	0.745053	0.0	0.000324	0.002105	0.161043	0.596407	0.0	0.000292	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
15	0.003173	0.14742	0.688385	0.0	0.000345	0.003272	0.158281	0.793357	0.0	0.000259	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16	0.002258	0.161573	0.529293	0.0	0.000373	0.003849	0.140374	0.826192	0.0	0.000271	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17	0.00332	0.109239	0.754241	0.0	0.000298	0.002638	0.141529	0.623157	0.0	0.000341	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18	0.002711	0.145419	0.625876	0.0	0.000348	0.00369	0.127022	0.808635	0.0	0.000277	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19	0.003714	0.096666	0.814768	0.0	0.000273	0.002661	0.124977	0.651076	0.0	0.00032	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
20	0.003409	0.116767	0.757866	0.0	0.000303	0.003631	0.15701	0.734336	0.0	0.000346	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
21	0.003332	0.105987	0.748031	0.0	0.000305	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
22	0.00371	0.096883	0.814834	0.0	0.000272	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
23	0.00174	0.140773	0.546222	0.0	0.000275	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
24	0.002587	0.109174	0.63308	0.0	0.000326	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
25	0.003623	0.126982	0.650032	0.0	0.000437	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
26	0.000745	0.206945	0.252603	0.013029	0.000295	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
27	0.003086	0.131114	0.605479	0.0	0.000418	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
28	0.000719	0.213052	0.267428	0.008436	0.000267	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
29	0.002225	0.165676	0.453346	0.000004	0.000451	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
30	0.001525	0.148101	0.486382	0.000001	0.000283	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
31	0.001595	0.203518	0.42671	0.000015	0.000349	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32	0.002126	0.176337	0.704722	0.0	0.000221	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
33	0.001402	0.171176	0.371599	0.000193	0.000361	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
34	0.00281	0.146148	0.659787	0.0	0.00033	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
35	0.000838	0.224774	0.312282	0.001952	0.000263	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
36	0.004076	0.113552	0.816263	0.0	0.000298	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37	0.002754	0.143618	0.690735	0.0	0.000297	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
38	0.003623	0.134008	0.723071	0.0	0.000357	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
39	0.001385	0.20143	0.374677	0.000169	0.000354	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

40 rows × 70 columns

# resettlement_stats.iloc[:, resettlement_stats.columns.get_level_values(1)=='slope']
# data = resettlement_stats.xs('slope', level=1, axis=1)
# len(data.columns)

data = resettlement_stats.xs('slope', level=1, axis=1)
data = data*108/12

# data.dropna(ignore_index=T).reset_index(drop=True)#.reset_index(drop=True, inplace=True)
data
# data.median().iloc[::-1].index

	Leyte	Samar	Biliran	Cebu	Aklan	Antique	Mis Or	Lanao DN	Albay	Catanduanes	Laguna	Davao	Bulacan	Rizal
0	0.031378	0.029387	0.03179	0.032625	0.029412	0.03183	0.025002	0.025861	0.039682	0.029325	0.052604	0.027019	0.076641	0.065657
1	0.02909	0.02909	0.02909	0.02886	0.03315	0.03315	0.026123	0.026123	0.034828	0.034828	0.049601	0.02902	0.042161	0.049601
2	0.026278	0.034666	0.036738	0.015851	0.017977	0.024315	0.027235	0.017581	0.005628	0.012136	0.025165	-0.003049	0.029993	0.027959
3	0.025796	0.030258	0.030803	0.005932	0.017932	0.023812	0.025313	0.001206	0.009447	0.018878	0.029781	0.004781	0.025392	0.03351
4	0.024459	0.028304	-0.001608	0.006132	0.019519	0.018599	0.008441	0.008943	0.000089	NaN	0.027122	-0.001174	0.036974	NaN
5	0.02616	0.014044	0.01197	0.012208	0.022133	0.01859	0.018563	0.008729	0.001148	NaN	0.033046	0.013245	0.033894	NaN
6	0.022464	0.021682	0.010145	0.011822	0.017162	0.020059	0.015015	0.01546	0.0124	NaN	NaN	0.006513	NaN	NaN
7	0.026398	0.030337	0.007239	0.019808	0.016086	0.017198	0.016325	0.022071	0.005318	NaN	NaN	0.018594	NaN	NaN
8	0.029822	0.033963	0.000346	0.016582	0.021421	0.018446	0.017828	0.017197	0.01018	NaN	NaN	0.010816	NaN	NaN
9	0.023595	0.016767	-0.004085	0.005904	0.022399	0.020398	0.013913	NaN	0.009038	NaN	NaN	0.001939	NaN	NaN
10	0.029719	0.019125	0.001254	0.015721	0.012305	0.020482	0.012057	NaN	0.015801	NaN	NaN	NaN	NaN	NaN
11	0.027764	0.02393	0.025547	0.017321	0.018097	0.021205	0.011675	NaN	0.015811	NaN	NaN	NaN	NaN	NaN
12	0.023562	0.030717	0.030479	0.02051	0.013824	0.019993	0.008433	NaN	0.014675	NaN	NaN	NaN	NaN	NaN
13	0.03197	0.017611	0.037162	0.017887	0.011644	0.019548	0.021147	NaN	0.016476	NaN	NaN	NaN	NaN	NaN
14	0.031544	0.018949	NaN	0.020401	NaN	0.019081	0.008602	NaN	0.009898	NaN	NaN	NaN	NaN	NaN
15	0.028555	0.029451	NaN	0.027158	NaN	0.021428	0.013569	NaN	0.005618	NaN	NaN	NaN	NaN	NaN
16	0.020326	0.034641	NaN	0.027301	NaN	0.020808	0.007683	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17	0.029877	0.023739	NaN	0.027221	NaN	0.023345	0.004854	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18	0.024402	0.033211	NaN	0.005388	NaN	0.021428	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19	0.03343	0.023949	NaN	0.017102	NaN	0.018047	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
20	0.030681	0.032677	NaN	0.011592	NaN	0.017839	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
21	0.029985	NaN	NaN	0.005061	NaN	0.00949	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
22	0.033389	NaN	NaN	0.027963	NaN	0.017688	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
23	0.015656	NaN	NaN	0.030017	NaN	0.015717	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
24	0.023279	NaN	NaN	0.009638	NaN	0.01858	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
25	0.032611	NaN	NaN	NaN	NaN	0.018477	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
26	0.006709	NaN	NaN	NaN	NaN	0.022115	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
27	0.027772	NaN	NaN	NaN	NaN	0.017787	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
28	0.006472	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
29	0.020024	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
30	0.013723	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
31	0.014355	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32	0.019131	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
33	0.012616	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
34	0.025286	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
35	0.007545	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
36	0.036684	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37	0.024785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
38	0.032609	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
39	0.012466	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

index_sort = data[2:].mean().sort_values(ascending = False).index
data_sorted = data[index_sort]
data_sorted

	Bulacan	Rizal	Laguna	Samar	Leyte	Antique	Aklan	Cebu	Catanduanes	Biliran	Mis Or	Lanao DN	Albay	Davao
0	0.076641	0.065657	0.052604	0.029387	0.031378	0.03183	0.029412	0.032625	0.029325	0.03179	0.025002	0.025861	0.039682	0.027019
1	0.042161	0.049601	0.049601	0.02909	0.02909	0.03315	0.03315	0.02886	0.034828	0.02909	0.026123	0.026123	0.034828	0.02902
2	0.029993	0.027959	0.025165	0.034666	0.026278	0.024315	0.017977	0.015851	0.012136	0.036738	0.027235	0.017581	0.005628	-0.003049
3	0.025392	0.03351	0.029781	0.030258	0.025796	0.023812	0.017932	0.005932	0.018878	0.030803	0.025313	0.001206	0.009447	0.004781
4	0.036974	NaN	0.027122	0.028304	0.024459	0.018599	0.019519	0.006132	NaN	-0.001608	0.008441	0.008943	0.000089	-0.001174
5	0.033894	NaN	0.033046	0.014044	0.02616	0.01859	0.022133	0.012208	NaN	0.01197	0.018563	0.008729	0.001148	0.013245
6	NaN	NaN	NaN	0.021682	0.022464	0.020059	0.017162	0.011822	NaN	0.010145	0.015015	0.01546	0.0124	0.006513
7	NaN	NaN	NaN	0.030337	0.026398	0.017198	0.016086	0.019808	NaN	0.007239	0.016325	0.022071	0.005318	0.018594
8	NaN	NaN	NaN	0.033963	0.029822	0.018446	0.021421	0.016582	NaN	0.000346	0.017828	0.017197	0.01018	0.010816
9	NaN	NaN	NaN	0.016767	0.023595	0.020398	0.022399	0.005904	NaN	-0.004085	0.013913	NaN	0.009038	0.001939
10	NaN	NaN	NaN	0.019125	0.029719	0.020482	0.012305	0.015721	NaN	0.001254	0.012057	NaN	0.015801	NaN
11	NaN	NaN	NaN	0.02393	0.027764	0.021205	0.018097	0.017321	NaN	0.025547	0.011675	NaN	0.015811	NaN
12	NaN	NaN	NaN	0.030717	0.023562	0.019993	0.013824	0.02051	NaN	0.030479	0.008433	NaN	0.014675	NaN
13	NaN	NaN	NaN	0.017611	0.03197	0.019548	0.011644	0.017887	NaN	0.037162	0.021147	NaN	0.016476	NaN
14	NaN	NaN	NaN	0.018949	0.031544	0.019081	NaN	0.020401	NaN	NaN	0.008602	NaN	0.009898	NaN
15	NaN	NaN	NaN	0.029451	0.028555	0.021428	NaN	0.027158	NaN	NaN	0.013569	NaN	0.005618	NaN
16	NaN	NaN	NaN	0.034641	0.020326	0.020808	NaN	0.027301	NaN	NaN	0.007683	NaN	NaN	NaN
17	NaN	NaN	NaN	0.023739	0.029877	0.023345	NaN	0.027221	NaN	NaN	0.004854	NaN	NaN	NaN
18	NaN	NaN	NaN	0.033211	0.024402	0.021428	NaN	0.005388	NaN	NaN	NaN	NaN	NaN	NaN
19	NaN	NaN	NaN	0.023949	0.03343	0.018047	NaN	0.017102	NaN	NaN	NaN	NaN	NaN	NaN
20	NaN	NaN	NaN	0.032677	0.030681	0.017839	NaN	0.011592	NaN	NaN	NaN	NaN	NaN	NaN
21	NaN	NaN	NaN	NaN	0.029985	0.00949	NaN	0.005061	NaN	NaN	NaN	NaN	NaN	NaN
22	NaN	NaN	NaN	NaN	0.033389	0.017688	NaN	0.027963	NaN	NaN	NaN	NaN	NaN	NaN
23	NaN	NaN	NaN	NaN	0.015656	0.015717	NaN	0.030017	NaN	NaN	NaN	NaN	NaN	NaN
24	NaN	NaN	NaN	NaN	0.023279	0.01858	NaN	0.009638	NaN	NaN	NaN	NaN	NaN	NaN
25	NaN	NaN	NaN	NaN	0.032611	0.018477	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
26	NaN	NaN	NaN	NaN	0.006709	0.022115	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
27	NaN	NaN	NaN	NaN	0.027772	0.017787	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
28	NaN	NaN	NaN	NaN	0.006472	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
29	NaN	NaN	NaN	NaN	0.020024	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
30	NaN	NaN	NaN	NaN	0.013723	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
31	NaN	NaN	NaN	NaN	0.014355	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
32	NaN	NaN	NaN	NaN	0.019131	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
33	NaN	NaN	NaN	NaN	0.012616	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
34	NaN	NaN	NaN	NaN	0.025286	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
35	NaN	NaN	NaN	NaN	0.007545	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
36	NaN	NaN	NaN	NaN	0.036684	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
37	NaN	NaN	NaN	NaN	0.024785	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
38	NaN	NaN	NaN	NaN	0.032609	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
39	NaN	NaN	NaN	NaN	0.012466	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Violin Plot

# plt.figure(figsize = (18,8), dpi = 150)
# plt.grid(alpha = 0.5)
# sns.violinplot(data_sorted[2:], palette="Spectral",
#                scale='count', inner='quartile',saturation=1, alpha = 0.3)
# sns.swarmplot(data_sorted[2:], palette = 'plasma')
# plt.plot(np.arange(len(data.columns)), data_sorted[0:1].values.T, 'c^', label = 'Provincial Baseline', ms = 15)
# plt.plot(np.arange(len(data.columns)), data_sorted[1:2].values.T, 'm^', label = 'Regional Baseline', ms = 15)
# # plt.yscale('log')
# plt.ylim(-0.002, 0.010)
# plt.title('NTL rate of change of post-disaster resettlement sites in the Philippines (2014-2022)')
# plt.legend()

Boxplot

# plt.figure(figsize = (18,8), dpi = 150)
# plt.grid(alpha = 0.5)
# sns.boxplot(data_sorted[2:], palette="Spectral", notch=True, showcaps=False,
#                 flierprops={"marker": "x"}, medianprops={"color": "r", "linewidth": 5})
# # sns.swarmplot(data_sorted[2:], palette = 'plasma')
# # for i in range(len(data.columns)):
# #     plt.plot([0,len(data.columns)], [0, data_sorted[2:].median()[i]], color = plt.cm.Spectral(i/len(data.columns)))

# plt.plot(np.arange(len(data.columns)), data_sorted[0:1].values.T, 'c^', label = 'Provincial Baseline', ms = 15)
# plt.plot(np.arange(len(data.columns)), data_sorted[1:2].values.T, 'm^', label = 'Regional Baseline', ms = 15)
# plt.title('NTL rate of change of post-disaster resettlement sites in the Philippines (2014-2022)')
# plt.legend()

plt.figure(figsize = (10,7), dpi = 150)
plt.grid(alpha = 0.5)
sns.boxplot(data_sorted[2:], palette="Spectral", orient = 'h',
                flierprops={"marker": "x"}, medianprops={"color": "r", "linewidth": 5})
sns.swarmplot(data_sorted[2:], palette = 'gnuplot2', orient = 'h', alpha = 0.3)
# for i in range(len(data.columns)):
#     plt.plot([0,len(data.columns)], [0, data_sorted[2:].median()[i]], color = plt.cm.Spectral(i/len(data.columns)))

plt.plot(data_sorted[0:1].values.T, np.arange(len(data.columns)), 'cx', label = 'Provincial Baseline', ms = 10)
plt.plot(data_sorted[1:2].values.T, np.arange(len(data.columns)), 'mx', label = 'Regional Baseline', ms = 10)
# plt.title('NTL rate of change of post-disaster resettlement sites in the Philippines (2014-2022)')
# plt.ylabel("Resettlement Sites")
plt.xlabel("NTL Annual Rate of Change")
plt.legend()

<matplotlib.legend.Legend at 0x7fae19aae050>

index_sort = data[2:].mean().sort_values(ascending = True).index
data_sorted = data[index_sort]
data_sorted
plt.rcParams["figure.dpi"] = 150

d = {'Provincial Baseline': data_sorted[0:1].values[0] - data_sorted[2:].mean().values,
     'Regional Baseline': data_sorted[1:2].values[0] - data_sorted[2:].mean().values}
diff = pd.DataFrame(data=d, index=data_sorted.columns)
diff.plot(y=['Provincial Baseline', 'Regional Baseline'], kind = 'barh',
          color={'Provincial Baseline': "cyan", 'Regional Baseline': "magenta"},
          figsize = (10,6), width=0.6)
plt.grid(alpha = 0.5)

plt.xlabel("Rate of Change difference from the baseline", fontsize = 10)
plt.tight_layout()
plt.show()
# plt.xlim(0,0.08)
# data_sorted[1:2].values[0] - data_sorted[2:].mean().values

data_sorted.columns

Index(['Bulacan', 'Rizal', 'Laguna', 'Samar', 'Leyte', 'Antique', 'Aklan',
       'Cebu', 'Catanduanes', 'Biliran', 'Mis Or', 'Lanao DN', 'Albay',
       'Davao'],
      dtype='object')

plt.bar(np.arange(len(data.columns)),diff['Provincial difference'])
plt.bar(np.arange(len(data.columns)),diff['Provincial difference'])

<BarContainer object of 14 artists>

sns.barplot()

array(['Bulacan', 'Rizal', 'Laguna', 'Samar', 'Leyte', 'Antique', 'Aklan',
       'Cebu', 'Catanduanes', 'Biliran', 'Mis Or', 'Lanao DN', 'Albay',
       'Davao'], dtype=object)

data_sorted[2:].mean().values

array([0.03156308, 0.03073461, 0.02877841, 0.02621171, 0.02399731,
       0.01940291, 0.01754167, 0.01628347, 0.01550688, 0.01549916,
       0.01441563, 0.01302667, 0.00939475, 0.00645808])

for i in range(len(data.columns)):
    data_sorted[2:].loc(i)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py in _get_axis_number(cls, axis)
    553         try:
--> 554             return cls._AXIS_TO_AXIS_NUMBER[axis]
    555         except KeyError:

KeyError: 2

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-146-3baea1c95da4> in <cell line: 1>()
      1 for i in range(len(data.columns)):
----> 2     data_sorted[2:].loc(i)

/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py in __call__(self, axis)
    666 
    667         if axis is not None:
--> 668             axis = self.obj._get_axis_number(axis)
    669         new_self.axis = axis
    670         return new_self

/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py in _get_axis_number(cls, axis)
    554             return cls._AXIS_TO_AXIS_NUMBER[axis]
    555         except KeyError:
--> 556             raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
    557 
    558     @final

ValueError: No axis named 2 for object type DataFrame

plt.figure(figsize = (10,8), dpi = 150)
plt.grid(alpha = 0.5)
# sns.boxplot(data_sorted[2:], palette="Spectral", orient = 'h',
#                 flierprops={"marker": "x"}, medianprops={"color": "r", "linewidth": 5})
# # sns.swarmplot(data_sorted[2:], palette = 'gnuplot2', orient = 'h', alpha = 0.3)
# for i in range(len(data.columns)):
#     plt.plot([0,len(data.columns)], [0, data_sorted[2:].median()[i]], color = plt.cm.Spectral(i/len(data.columns)))

plt.barh((data_sorted[0:1].values[0] - data_sorted[2:].mean().values, width = 2)
# plt.bar(data_sorted[1:2].values.T - data_sorted[2:].mean(), np.arange(len(data.columns)))
# plt.title('NTL rate of change of post-disaster resettlement sites in the Philippines (2014-2022)')
plt.ylabel("Resettlement Sites")
plt.xlabel("NTL Annual Rate of Change")
plt.legend()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-169-f16e13675570> in <cell line: 9>()
      7 #     plt.plot([0,len(data.columns)], [0, data_sorted[2:].median()[i]], color = plt.cm.Spectral(i/len(data.columns)))
      8 
----> 9 plt.barh(([data_sorted[0:1].values[0] - data_sorted[2:].mean().values]), width = 2)
     10 # plt.bar(data_sorted[1:2].values.T - data_sorted[2:].mean(), np.arange(len(data.columns)))
     11 # plt.title('NTL rate of change of post-disaster resettlement sites in the Philippines (2014-2022)')

/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py in barh(y, width, height, left, align, data, **kwargs)
   2455         y, width, height=0.8, left=None, *, align='center',
   2456         data=None, **kwargs):
-> 2457     return gca().barh(
   2458         y, width, height=height, left=left, align=align,
   2459         **({"data": data} if data is not None else {}), **kwargs)

/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py in barh(self, y, width, height, left, align, data, **kwargs)
   2647         """
   2648         kwargs.setdefault('orientation', 'horizontal')
-> 2649         patches = self.bar(x=left, height=height, width=width, bottom=y,
   2650                            align=align, data=data, **kwargs)
   2651         return patches

/usr/local/lib/python3.10/dist-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
   1440     def inner(ax, *args, data=None, **kwargs):
   1441         if data is None:
-> 1442             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1443 
   1444         bound = new_sig.bind(ax, *args, **kwargs)

/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
   2478                    hatch, patch_labels)
   2479         for l, b, w, h, c, e, lw, htch, lbl in args:
-> 2480             r = mpatches.Rectangle(
   2481                 xy=(l, b), width=w, height=h,
   2482                 facecolor=c,

/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py in wrapper(*args, **kwargs)
    452                 "parameter will become keyword-only %(removal)s.",
    453                 name=name, obj_type=f"parameter of {func.__name__}()")
--> 454         return func(*args, **kwargs)
    455 
    456     # Don't modify *func*'s signature, as boilerplate.py needs it.

/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py in __init__(self, xy, width, height, angle, rotation_point, **kwargs)
    712             %(Patch:kwdoc)s
    713         """
--> 714         super().__init__(**kwargs)
    715         self._x0 = xy[0]
    716         self._y0 = xy[1]

/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py in wrapper(*args, **kwargs)
    452                 "parameter will become keyword-only %(removal)s.",
    453                 name=name, obj_type=f"parameter of {func.__name__}()")
--> 454         return func(*args, **kwargs)
    455 
    456     # Don't modify *func*'s signature, as boilerplate.py needs it.

/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py in __init__(self, edgecolor, facecolor, color, linewidth, linestyle, antialiased, hatch, fill, capstyle, joinstyle, **kwargs)
     91         self.set_fill(fill)
     92         self.set_linestyle(linestyle)
---> 93         self.set_linewidth(linewidth)
     94         self.set_antialiased(antialiased)
     95         self.set_hatch(hatch)

/usr/local/lib/python3.10/dist-packages/matplotlib/patches.py in set_linewidth(self, w)
    392         if w is None:
    393             w = mpl.rcParams['patch.linewidth']
--> 394         self._linewidth = float(w)
    395         self._dash_pattern = mlines._scale_dashes(
    396             *self._unscaled_dash_pattern, w)

TypeError: only size-1 arrays can be converted to Python scalars

Summary

fig = plt.figure(constrained_layout=True, figsize = (12,5), dpi = 150)
spec = gridspec.GridSpec(ncols=2, nrows=2, figure=fig)

ax1 = fig.add_subplot(spec[:,0])
ax1.grid(alpha = 0.5)
for i in range(len(data.columns)):
    ax1.plot([2014.55,2022.55], [0, data_sorted[2:].median()[i]], lw = 3,
            color = 'k')
for i in range(len(data.columns)):
    ax1.plot([2014.5,2022.5], [0, data_sorted[2:].median()[i]], lw = 3,
             color = plt.cm.Spectral(i/len(data.columns)), label = str(data_sorted.columns[i]))
ax1.set_ylabel('NTL Rate of Change')
fig.legend(fontsize=10, ncol = 7, bbox_to_anchor= (0, 1.01, 2.1, 0.2), loc="lower left",
            bbox_transform=ax1.transAxes, fancybox = True, mode='expand')

ax2 = fig.add_subplot(spec[0,1])
ax2.set_ylabel('Difference from Provincial NTL')
sns.barplot(data_sorted[0:1]-data_sorted[2:].median(), palette="Spectral")
ax2.grid(alpha = 0.5)
ax2.set_ylim(0,0.005)
plt.xticks([])

ax3 = fig.add_subplot(spec[1,1])
ax3.set_ylabel('Difference from Regional NTL')
sns.barplot(data_sorted[1:2]-data_sorted[2:].median(), palette="Spectral")
ax3.grid(alpha = 0.5)
ax3.set_ylim(0,0.005)
plt.xticks([])

plt.tight_layout()
plt.show()

Seasonality Analysis

Leyte_df['Seasonality']

	Leyte Province	Region VIII	Kawayan Ville	Villa Diana	Ridge View Park	New Hope Village	Knightsridge Residences	Villa Sofia	North Hill Arbours	Salvacion Heights	...	Villa Soledad	Virlyn Heights Subdivision	Tabango Ville	Dulag Town Ville	Barugo Town Ville	Villa Verde	Capoocan Village	Dagami Town Ville	Sta. Fe Village Site 1	Cool Spring Residences
date
2014-01-01	-0.103132	-0.098371	0.020012	0.027763	0.031110	-0.004142	0.041233	0.018104	0.000407	0.017866	...	0.113296	0.050744	0.059906	0.103575	0.078341	0.132085	0.057732	0.101062	0.034075	0.099044
2014-02-01	-0.041838	-0.038405	0.012527	-0.039290	-0.013462	-0.038194	-0.027929	-0.024743	-0.013678	-0.002433	...	-0.050066	0.007425	-0.005925	0.023387	0.055096	-0.029619	-0.084467	0.037515	-0.026614	-0.001270
2014-03-01	0.059328	0.042988	-0.181156	-0.148180	-0.146681	-0.153052	-0.152547	-0.172574	-0.173536	-0.089915	...	-0.054252	-0.103459	-0.103440	-0.090509	-0.138855	-0.079634	-0.083616	-0.086136	-0.138984	-0.097518
2014-04-01	0.144660	0.132693	-0.171002	-0.211562	-0.231677	-0.217995	-0.206950	-0.173412	-0.169973	-0.227916	...	-0.164616	-0.137074	-0.167036	-0.163917	-0.137877	-0.134742	-0.252763	-0.161639	-0.170366	-0.076188
2014-05-01	0.124580	0.106606	0.295289	0.397323	0.331590	0.354056	0.277385	0.399490	0.301009	0.262256	...	0.189255	0.332070	0.237063	-0.022846	-0.098588	-0.024973	0.259477	0.028918	0.263712	0.213953
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-07-01	-0.024151	-0.014741	0.600297	0.604459	0.830588	0.878136	0.707635	0.594835	0.648098	0.742986	...	0.109342	0.236655	0.296021	0.127160	0.704305	0.423533	0.608077	0.605084	0.677630	0.233269
2022-08-01	0.027401	0.030899	-0.030288	-0.040270	-0.079603	-0.085427	-0.023550	-0.034799	-0.039374	-0.055678	...	-0.038089	0.025407	0.073328	-0.029172	-0.048232	0.044926	0.013724	-0.026534	-0.029986	0.097171
2022-09-01	0.008095	0.007868	-0.155892	-0.143988	-0.153193	-0.150404	-0.165988	-0.143838	-0.147396	-0.124520	...	-0.068896	-0.046771	-0.078371	-0.063162	-0.071228	-0.070125	-0.124132	-0.101538	-0.116430	-0.100760
2022-10-01	-0.066057	-0.030445	-0.064588	-0.067525	-0.104706	-0.096208	-0.079508	-0.054159	-0.061379	-0.076677	...	0.020772	-0.020538	-0.043537	0.014142	-0.066395	-0.019687	-0.053299	-0.011152	-0.068218	-0.058186
2022-11-01	-0.081935	-0.078388	-0.059587	-0.068058	-0.117588	-0.108914	-0.072068	-0.054736	-0.059380	-0.076449	...	0.019688	-0.049045	-0.023298	0.048909	-0.031155	-0.030454	-0.052250	-0.068668	-0.072077	-0.056786

107 rows × 40 columns

plt.figure(dpi = 150, figsize = (18,6))
plt.grid(alpha = 0.5)
seasonal = Leyte_df['Seasonality'][:12].rename(index=lambda x: x.strftime('%B'))
ormalized_seasonal=2*((seasonal-seasonal.min())/(seasonal.max()-seasonal.min())-0.5)
plt.plot(seasonal)
plt.show()

plt.figure(dpi = 150, figsize = (25,10))
sns.heatmap(seasonal, cmap = 'RdYlBu_r', annot=True, fmt= '0.1f',)
plt.ylabel('Month')
plt.show()

plt.figure(dpi = 150, figsize = (25,10))
sns.heatmap(normalized_seasonal, cmap = 'RdYlBu_r', annot=True, fmt= '0.1f',)
plt.ylabel('Month')
plt.show()

<Axes: ylabel='date'>

def seasonal_variation(df):
    seasonal = df['Seasonality'][:12].rename(index=lambda x: x.strftime('%B'))
    normalized_seasonal=2*((seasonal-seasonal.min())/(seasonal.max()-seasonal.min())-0.5)

    # plt.figure(dpi = 150, figsize = (25,10))
    # sns.heatmap(seasonal, cmap = 'RdYlBu_r', annot=True, fmt= '0.1f',)
    # plt.ylabel('Month')
    # plt.show()

    plt.figure(dpi = 150, figsize = (25,10))
    sns.heatmap(normalized_seasonal, cmap = 'RdYlBu_r', annot=True, fmt= '0.1f',)
    plt.ylabel('Month')
    plt.show()

seasonal_variation(Cebu_df)

seasonal_variation(Samar_df)

seasonal_variation(MisOr_df)

seasonal = Leyte_df['Seasonality'][:12].rename(index=lambda x: x.strftime('%b'))
normalized_seasonal=(seasonal-seasonal.min())/(seasonal.max()-seasonal.min())
normalized_seasonal

	Leyte Province	Region VIII	Kawayan Ville	Villa Diana	Ridge View Park	New Hope Village	Knightsridge Residences	Villa Sofia	North Hill Arbours	Salvacion Heights	...	Villa Soledad	Virlyn Heights Subdivision	Tabango Ville	Dulag Town Ville	Barugo Town Ville	Villa Verde	Capoocan Village	Dagami Town Ville	Sta. Fe Village Site 1	Cool Spring Residences
date
Jan	0.000000	0.000000	0.370582	0.384492	0.316073	0.292757	0.355136	0.373755	0.339204	0.320262	...	0.816847	0.550886	0.592903	0.918974	0.370234	0.584083	0.407595	0.438261	0.355094	0.737811
Feb	0.247360	0.259519	0.362464	0.312926	0.277943	0.265461	0.288208	0.327229	0.324834	0.301233	...	0.422947	0.481730	0.479399	0.643488	0.346848	0.353321	0.254528	0.367437	0.294278	0.541864
Mar	0.655633	0.611774	0.152381	0.196708	0.163979	0.173389	0.167618	0.166705	0.161741	0.219226	...	0.412852	0.304712	0.311268	0.252195	0.151719	0.281946	0.255444	0.229627	0.181672	0.353857
Apr	1.000000	1.000000	0.163396	0.129060	0.091268	0.121330	0.114973	0.165796	0.165376	0.089863	...	0.146741	0.251049	0.201619	0.000000	0.152702	0.203303	0.073370	0.145477	0.150224	0.395523
May	0.918965	0.887100	0.669167	0.778924	0.573124	0.579893	0.583655	0.787884	0.645888	0.549357	...	1.000000	1.000000	0.898349	0.484652	0.192230	0.359951	0.624759	0.357855	0.585213	0.962269
Jun	0.580729	0.565601	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.513748	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Jul	0.318739	0.361931	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	0.807313	0.847678	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Aug	0.526785	0.559454	0.316024	0.311880	0.221362	0.227598	0.292447	0.316310	0.298617	0.251321	...	0.451826	0.510436	0.616043	0.462920	0.242892	0.459701	0.360224	0.296053	0.290899	0.734153
Sep	0.448872	0.459780	0.179785	0.201182	0.158408	0.175512	0.154612	0.197908	0.188410	0.186787	...	0.377544	0.395209	0.354491	0.346148	0.219756	0.295516	0.211832	0.212460	0.204273	0.347525
Oct	0.149622	0.293970	0.278819	0.282791	0.199887	0.218956	0.238297	0.295288	0.276168	0.231636	...	0.593752	0.437089	0.414549	0.611724	0.224619	0.367494	0.288078	0.313197	0.252586	0.430686
Nov	0.085545	0.086482	0.284243	0.282222	0.188867	0.208770	0.245497	0.294660	0.278207	0.231850	...	0.591139	0.391580	0.449445	0.731166	0.260073	0.352129	0.289207	0.249094	0.248719	0.433422
Dec	0.062208	0.023140	0.409651	0.378140	0.282604	0.289262	0.342380	0.323616	0.387017	0.260633	...	0.903030	0.468146	0.557310	0.792668	0.335929	0.489624	0.380371	0.298050	0.288412	0.595018

12 rows × 40 columns

lowerLimit = 0
pal = list(sns.color_palette(palette='hsv', n_colors=len(seasonal)).as_hex())

plt.figure(figsize=(8,8))
ax = plt.subplot(111, polar=True)
heights = normalized_seasonal[normalized_seasonal.columns[2:]].mean(axis=1)


width = 2*np.pi / 12
indexes = list(range(1, len(seasonal.index)+1))
angles = [element * width for element in indexes]
ax.plot(angles, normalized_seasonal.iloc[:, 0], 'c--', label = 'Provincial Baseline')
ax.plot(angles, normalized_seasonal.iloc[:, 1], 'm--', label = 'Regional Baseline')


bars = ax.bar(x=angles, height=heights, width=width, bottom=lowerLimit,
                  linewidth=1, edgecolor="white", color=plt.cm.RdYlBu_r(heights)) #color = pal)#

labelPadding = 0.1

for bar, angle, height, label in zip(bars, angles, heights, seasonal.index):
        rotation = np.rad2deg(angle)
        alignment = ""

        if angle >= np.pi/2 and angle < 3*np.pi/2:
            alignment = "right"
            rotation = rotation + 180
        else:
            alignment = "left"

        ax.text(x=angle, y = 1.1,#y=lowerLimit + bar.get_height() + labelPadding,
                s=label, ha=alignment, va='center') #rotation=rotation,
                # rotation_mode="anchor")

        ax.set_thetagrids([], labels=[])
ax.legend()

<matplotlib.legend.Legend at 0x7fe2abd34430>

def get_df_name(df):
    name =[x for x in globals() if globals()[x] is df][0]
    return str(name[:-3])

def seasonal_circle(df):

    seasonal = df['Seasonality'][:12].rename(index=lambda x: x.strftime('%b'))
    normalized_seasonal=(seasonal-seasonal.min())/(seasonal.max()-seasonal.min())

    lowerLimit = 0
    pal = list(sns.color_palette(palette='hsv', n_colors=len(seasonal)).as_hex())

    plt.figure(figsize=(5,4), dpi = 150)
    ax = plt.subplot(111, polar=True)
    heights = normalized_seasonal[normalized_seasonal.columns[2:]].mean(axis=1)


    width = 2*np.pi / 12
    indexes = list(range(1, len(seasonal.index)+1))
    angles = [element * width for element in indexes]
    ax.plot(angles, normalized_seasonal.iloc[:, 0], 'co--', label = 'Provincial Baseline', lw = 2)
    ax.plot(angles, normalized_seasonal.iloc[:, 1], 'mo--', label = 'Regional Baseline', lw = 2)
    # ax.plot(angles, heights, 'ks-', label = 'Resettlement Sites', lw = 3, alpha = 0.5)

    bars = ax.bar(x=angles, height=heights, width=width, bottom=lowerLimit, label = 'Resettlement Sites',
                      linewidth=1, edgecolor="white", color=plt.cm.RdYlBu_r(heights)) #color = pal)#

    labelPadding = 0.1

    for bar, angle, height, label in zip(bars, angles, heights, seasonal.index):
            rotation = np.rad2deg(angle)
            alignment = ""

            if angle >= np.pi/2 and angle < 3*np.pi/2:
                alignment = "right"
                rotation = rotation + 180
            else:
                alignment = "left"

            ax.text(x=angle, y = 1.1,#y=lowerLimit + bar.get_height() + labelPadding,
                    s=label, ha=alignment, va='center') #rotation=rotation,
                    # rotation_mode="anchor")

            ax.set_thetagrids([], labels=[])
    ax.legend(fontsize = 8, title = get_df_name(df),  bbox_to_anchor= (0, 0.09, 0.95, 0.2), fancybox = True)
    return ax


seasonal_circle(Leyte_df)

<PolarAxes: >

Resettlement Seasonality

Yolanda

seasonal_circle(Leyte_df)

<PolarAxes: >

seasonal_circle(Samar_df)

<PolarAxes: >

seasonal_circle(Cebu_df)

<PolarAxes: >

seasonal_circle(Biliran_df)

<PolarAxes: >

Sendong

seasonal_circle(MisOr_df)

<PolarAxes: >

seasonal_circle(Lanao_df)

<PolarAxes: >

Pablo

seasonal_circle(Davao_df)

<PolarAxes: >

Near-Boracay (Yolanda)

seasonal_circle(Aklan_df)

<PolarAxes: >

seasonal_circle(Antique_df)

<PolarAxes: >

Bicol (Multi-storm)

seasonal_circle(Albay_df)

<PolarAxes: >

seasonal_circle(Catanduanes_df)

<PolarAxes: >

Ondoy

seasonal_circle(Laguna_df)

<PolarAxes: >

seasonal_circle(Bulacan_df)

<PolarAxes: >

seasonal_circle(Rizal_df)

<PolarAxes: >